Fully Managed (SaaS) vs. Partially Managed (PaaS) Cloud Services for Data Streaming with Kafka and Flink

The cloud revolution has transformed how businesses deploy, scale, and manage data streaming solutions. While Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS) cloud models are often used interchangeably in marketing, their distinctions have significant implications for operational efficiency, cost, and scalability. In the context of data streaming around Apache Kafka and Flink, understanding these differences and recognizing common misconceptions—such as the overuse of the term “serverless”—can help you make an informed decision. Additionally, the emergence of Bring Your Own Cloud (BYOC) offers yet another option, providing organizations with enhanced control and flexibility in their cloud environments.

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The Data Streaming Landscape: Kafka, Flink, Cloud, and More

The Data Streaming Landscape 2025 highlights how data streaming has evolved into a key software category, moving from niche adoption to a fundamental part of modern data architecture.

With frameworks like Apache Kafka and Flink at its core, the landscape now spans self-managed, BYOC, and fully managed SaaS solutions, driving real-time use cases, unifying transactional and analytical workloads, and enabling innovation across industries.

If you’re still grappling with the fundamentals of stream processing, this article is a must-read: “Stateless vs. Stateful Stream Processing with Kafka Streams and Apache Flink“.

What is SaaS in Data Streaming?

SaaS data streaming solutions are fully managed services where the provider handles all aspects of deployment, maintenance, scaling, and updates. SaaS offerings are designed for ease of use, providing a serverless experience where developers focus solely on building applications rather than managing infrastructure.

Characteristics of SaaS in Data Streaming

1. Serverless Architecture: Resources scale automatically based on demand. True SaaS solutions eliminate the need to provision or manage servers.
2. Low Operational Overhead: The provider manages hardware, software, and runtime configurations, including upgrades and security patches.
3. Pay-As-You-Go Pricing: Consumption-based pricing aligns costs directly with usage, reducing waste during low-demand periods.
4. Rapid Deployment: SaaS enables users to start processing streams within minutes, accelerating time-to-value.

Examples of SaaS in Data Streaming:

• Confluent Cloud: A fully managed Kafka platform offering serverless scaling, multi-tenancy, and a broad feature set for both stateless and stateful processing.
• Amazon Kinesis Data Analytics: A managed service for real-time analytics with automatic scaling.

What is PaaS in Data Streaming?

PaaS offerings sit between fully managed SaaS and infrastructure-as-a-service (IaaS). These solutions provide a platform to deploy and manage applications but still require significant user involvement for infrastructure management.

Characteristics of PaaS in Data Streaming

1. Partial Management: The provider offers tools and frameworks, but users must manage servers, clusters, and scaling policies.
2. Manual Configuration: Deployment involves provisioning VMs or containers, tuning parameters, and monitoring resource usage.
3. Complex Scaling: Scaling is not always automatic; users may need to adjust resource allocation based on workload changes.
4. Higher Overhead: PaaS requires more expertise and operational involvement, making it less accessible to teams without dedicated DevOps resources.

Examples of PaaS in Data Streaming (Kafka, Flink)

PaaS offerings in data streaming, while simplifying some infrastructure tasks, still require significant user involvement compared to fully serverless SaaS solutions. Below are three common examples, along with their benefits and pain points compared to serverless SaaS:

• Apache Flink (Self-Managed on Kubernetes Cloud Service like EKS, AKS or GKE)
  • Benefits: Full control over deployment and infrastructure customization.
  • Pain Points: High operational overhead for managing Kubernetes clusters, manual scaling, and complex resource tuning.
• Amazon Managed Service for Apache Flink (Amazon MSF)
  • Benefits: Simplifies infrastructure management and integrates with some other AWS services.
  • Pain Points: Users still handle job configuration, scaling optimization, and monitoring, making it less user-friendly than serverless SaaS solutions.
• Amazon MSK (Managed Streaming for Apache Kafka)
  • Benefits: Eases Kafka cluster maintenance and integrates with the AWS ecosystem.
  • Pain Points: Requires users to design and manage producers/consumers, manually configure scaling, and handle monitoring responsibilities. MSK also excludes support for Kafka if you have any operational issues with the Kafka piece of the infrastructure.

SaaS vs. PaaS: Key Differences

SaaS and PaaS differ in the level of management and user responsibility, with SaaS offering fully managed services for simplicity and PaaS requiring more user involvement for customization and control.

Feature	SaaS	PaaS
Infrastructure	Fully managed by the provider	Partially managed; user controls clusters
Scaling	Automatic and server less	Manual or semi-automatic scaling
Deployment Speed	Immediate, ready to use	Slower; requires configuration
Operational Complexity	Minimal	Moderate to high
Cost Model	Consumption-based, no idle costs	May incur idle resource costs

The big benefit of PaaS over SaaS is greater flexibility and control, allowing users to customize the platform, integrate with specific infrastructure, and optimize configurations to meet unique business or technical requirements. This level of control is often essential for organizations with strict compliance, security, or data sovereignty requirements.

SaaS is NOT Always Better than PaaS!

Be careful: The limitations and pain points of PaaS do NOT mean that SaaS is always better.

A concrete example: Amazon MSK Serverless simplifies Apache Kafka operations with automated scaling and infrastructure management but comes with significant limitations, including the lack of fully-managed connectors, advanced data governance tools, and native multi-language client support.

Amazon MSK also excludes Kafka engine support, leading to potential operational risks and cost unpredictability, especially when integrating with additional AWS services for a complete data streaming pipeline. I explored these challenges in more detail in my article “When NOT to choose Amazon MSK Serverless for Apache Kafka?“.

Bring Your Own Cloud (BYOC) as Alternative to PaaS

BYOC (Bring Your Own Cloud) offers a middle ground between fully managed SaaS and self-managed PaaS solutions, allowing organizations to host applications in their own VPCs.

BYOC provides enhanced control, security, and compliance while reducing operational complexity. This makes BYOC a strong alternative to PaaS for companies with strict regulatory or cost requirements.

As an example, here are the options of Confluent for deploying the data streaming platform: Serverless Confluent Cloud, Self-managed Confluent Platform (some consider this a PaaS if you leverage Confluent’s Kubernetes Operator and other automation / DevOps tooling) and WarpStream as BYOC offering:

While BYOC complements SaaS and PaaS, it can be a better choice when fully managed solutions don’t align with specific business needs. I wrote a detailed article about this topic: “Deployment Options for Apache Kafka: Self-Managed, Fully-Managed / Serverless and BYOC (Bring Your Own Cloud)“.

“Serverless” Claims: Don’t Trust the Marketing

Many cloud data streaming solutions are marketed as “serverless,” but this term is often misused. A truly serverless solution should:

1. Abstract Infrastructure: Users should never need to worry about provisioning, upgrading, or cluster sizing.
2. Scale Transparently: Resources should scale up or down automatically based on workload.
3. Eliminate Idle Costs: There should be no cost for unused capacity.

However, many products marketed as serverless still require some degree of infrastructure management or provisioning, making them closer to PaaS. For example:

• A so-called “serverless” PaaS solution may still require setting initial cluster sizes or monitoring node health.
• Some products charge for pre-provisioned capacity, regardless of actual usage.

Do Your Own Research

When evaluating data streaming solutions, dive into the technical documentation and ask pointed questions:

• Does the solution truly abstract infrastructure management?
• Are scaling policies automatic, or do they require manual configuration?
• Is there a minimum cost even during idle periods?

By scrutinizing these factors, you can avoid falling for misleading “serverless” claims and choose a solution that genuinely meets your needs.

Choosing the Right Model for Your Data Streaming Business: SaaS, PaaS, or BYOC

When adopting a data streaming platform, selecting the right model is crucial for aligning technology with your business strategy:

• Use SaaS (Software as a Service) if you prioritize ease of use, rapid deployment, and operational simplicity. SaaS is ideal for teams looking to focus entirely on application development without worrying about infrastructure.
• Use PaaS (Platform as a Service) if you require deep customization, control over resource allocation, or have unique workloads that SaaS offerings cannot address.
• Use BYOC (Bring Your Own Cloud) if your organization demands full control over its data but sees benefits in fully managed services. BYOC enables you to run the data plane within your cloud VPC, ensuring compliance, security, and architectural flexibility while leveraging SaaS functionality for the control plane .

In the rapidly evolving world of data streaming around Apache Kafka and Flink, SaaS data streaming platforms like Confluent Cloud provide the best of both worlds: the advanced features of tools like Apache Kafka and Flink, combined with the simplicity of a fully managed serverless experience. Whether you’re handling stateless stream processing or complex stateful analytics, SaaS ensures you’re scaling efficiently without operational headaches.

What deployment option do you use today for Kafka and Flink? Any changes planned in the future? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.